{"title":"多旋翼微型飞行器结构健康监测","authors":"P. Misra, Gopi Kandaswamy, Pragyan Mohapatra, Kriti Kumar Balamuralidhar, Prasant Misra Gopi Kandaswamy, Kriti Kumar","doi":"10.1145/3213526.3213531","DOIUrl":null,"url":null,"abstract":"Structural Health Monitoring (SHM) is a key troubleshooting methodology for assessing the working condition and health of (manned or unmanned) aerial vehicles; however, its understanding with respect to the multi-rotor class of Micro Aerial Vehicles (MAV) is limited. The portentous structural failure sources, in this case, are the two moving components: motors and propellers. In this paper, we undertake a detailed exercise of characterizing the common and frequent faults of these units using multi-modal sensing of vibration, acoustic noise, input power, and thrust profiles; and then use relevant features to perform a two-level diagnosis. Through our empirical fault studies on our custom designed test rig, we propose a set of befitting features in each sensory domain; which result in high fault detection and classification accuracy that exceeds 90%.","PeriodicalId":237910,"journal":{"name":"Proceedings of the 4th ACM Workshop on Micro Aerial Vehicle Networks, Systems, and Applications","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Structural Health Monitoring of Multi-Rotor Micro Aerial Vehicles\",\"authors\":\"P. Misra, Gopi Kandaswamy, Pragyan Mohapatra, Kriti Kumar Balamuralidhar, Prasant Misra Gopi Kandaswamy, Kriti Kumar\",\"doi\":\"10.1145/3213526.3213531\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Structural Health Monitoring (SHM) is a key troubleshooting methodology for assessing the working condition and health of (manned or unmanned) aerial vehicles; however, its understanding with respect to the multi-rotor class of Micro Aerial Vehicles (MAV) is limited. The portentous structural failure sources, in this case, are the two moving components: motors and propellers. In this paper, we undertake a detailed exercise of characterizing the common and frequent faults of these units using multi-modal sensing of vibration, acoustic noise, input power, and thrust profiles; and then use relevant features to perform a two-level diagnosis. Through our empirical fault studies on our custom designed test rig, we propose a set of befitting features in each sensory domain; which result in high fault detection and classification accuracy that exceeds 90%.\",\"PeriodicalId\":237910,\"journal\":{\"name\":\"Proceedings of the 4th ACM Workshop on Micro Aerial Vehicle Networks, Systems, and Applications\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 4th ACM Workshop on Micro Aerial Vehicle Networks, Systems, and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3213526.3213531\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 4th ACM Workshop on Micro Aerial Vehicle Networks, Systems, and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3213526.3213531","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Structural Health Monitoring of Multi-Rotor Micro Aerial Vehicles
Structural Health Monitoring (SHM) is a key troubleshooting methodology for assessing the working condition and health of (manned or unmanned) aerial vehicles; however, its understanding with respect to the multi-rotor class of Micro Aerial Vehicles (MAV) is limited. The portentous structural failure sources, in this case, are the two moving components: motors and propellers. In this paper, we undertake a detailed exercise of characterizing the common and frequent faults of these units using multi-modal sensing of vibration, acoustic noise, input power, and thrust profiles; and then use relevant features to perform a two-level diagnosis. Through our empirical fault studies on our custom designed test rig, we propose a set of befitting features in each sensory domain; which result in high fault detection and classification accuracy that exceeds 90%.